How to add ColSideColors to Heatmap.2 after biclusterization (row and column)

Question

I have the following code:

library(gplots) library(RColorBrewer); setwd("~/Desktop") mydata <- mtcars hclustfunc <- function(x) hclust(x, method="complete") distfunc <- function(x) dist(x,method="euclidean") d <- distfunc(mydata) fit <- hclustfunc(d) clusters <- cutree(fit, h=100) nofclust.height <- length(unique(as.vector(clusters))); # Colorings hmcols <- rev(redgreen(2750)) selcol <- colorRampPalette(brewer.pal(12,"Set3")) selcol2 <- colorRampPalette(brewer.pal(9,"Set1")) clustcol.height = selcol2(nofclust.height); heatmap.2(as.matrix(mydata), trace='none', dendrogram='both', key=F, Colv=T, scale='row', hclust=hclustfunc, distfun=distfunc, col=hmcols, symbreak=T, margins=c(7,10), keysize=0.1, lwid=c(5,0.5,3), lhei=c(0.05,0.5), lmat=rbind(c(5,0,4),c(3,1,2)), labRow=rownames(mydata), #ColSideColors=clustcol.height[clusters], # This line doesn't work RowSideColors=clustcol.height[clusters]) 

Which produce the following figure:

What I want to do is to cluster both in the row and in the column and show the clusters (RowSideColors and ColSideColors) next to the dendrogram. How can i achieve this?

At the moment, I am able to show RowSideColors , but not ColSideColors .

Answer 1

To show both RowSideColors and ColSideColors , you need to get the cluster assignments for the rows and columns of the matrix separately. Currently, the cluster object contains clusters that correspond only to rows.

 # set the custom distance and clustering functions, per your example hclustfunc <- function(x) hclust(x, method="complete") distfunc <- function(x) dist(x, method="euclidean") # perform clustering on rows and columns cl.row <- hclustfunc(distfunc(mydata)) cl.col <- hclustfunc(distfunc(t(mydata))) # extract cluster assignments; ie k=8 (rows) k=5 (columns) gr.row <- cutree(cl.row, 8) gr.col <- cutree(cl.col, 5) # require(RColorBrewer) col1 <- brewer.pal(8, "Set1") col2 <- brewer.pal(5, "Pastel1") # require(gplots) heatmap.2(as.matrix(mydata), hclustfun=hclustfunc, distfun=distfunc, RowSideColors=col1[gr.row], ColSideColors=col2[gr.col]) 

You can test clustering a priori with plot(cl.row) and plot(cl.col) . You can also play with the RColorBrewer library to select the most suitable color coding. Probably a consistent palette may be better to avoid over-coloring.